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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Pastrana Olarte, Lina Rocio. | - |
| dc.date.accessioned | 2022-10-05T14:51:58Z | - |
| dc.date.available | 2018-03-15 | - |
| dc.date.available | 2022-10-05T14:51:58Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.citation | Pastrana Olarte, L. R. (2017). Análisis de los métodos de predicción de índices bursátiles como herramienta decisoria de inversión [Trabajo de Grado Pregrado, Universidad de Pamplona]. Repositorio Hulago Universidad de Pamplona. http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3601 | es_CO |
| dc.identifier.uri | http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3601 | - |
| dc.description | Esta monografía presenta una revisión bibliográfica sobre los métodos que se han utilizado los últimos años para la predicción de los índices bursátiles. Los métodos que vamos a dar a conocer son aquellos que han ayudado por décadas a los inversionistas a tomar una mejor decisión a la hora de invertir. Empezamos desde los modelos lineales, por la fácil interpretación de sus elementos, tienen una considerable ventaja sobre otros, lo que ha hecho que sean utilizados en un sinnúmero de aplicaciones; una de ellas ha sido la predicción de series de tiempo financieras, de seguido se mostrara los modelos no lineales el cual utilizan modelos estadísticos no paramétricos y no lineales debido a que muchas relaciones importantes en el área de las finanzas tienen este tipo de relaciones. Las redes neuronales artificiales poseen la propiedad de capturar las características no lineales de los índices de bolsa y han demostrado que pueden ser entrenadas con una cantidad suficiente de información para identificar dichas relaciones no lineales entre los valores de entrada y salida. Por último, se dará a conocer los modelos híbridos que hace referencia a la combinación de dos o más elementos. En la predicción de la bolsa de valores muchos autores han realizado combinaciones de métodos o modelos de predicción buscando incorporar en el método híbrido las ventajas de cada uno de los modelos anteriores. | es_CO |
| dc.description.abstract | La autora no proporciona la información sobre este ítem. | es_CO |
| dc.format.extent | 30 | es_CO |
| dc.format.mimetype | application/pdf | es_CO |
| dc.language.iso | es | es_CO |
| dc.publisher | Universidad de Pamplona – Facultad de Ingenierias y Arquitectura. | es_CO |
| dc.subject | Indices Bursátiles. | es_CO |
| dc.subject | Métodos o modelos de predicción. | es_CO |
| dc.subject | Herramientas para la inversión. | es_CO |
| dc.subject | Bolsa de valores. | es_CO |
| dc.title | Análisis de los métodos de predicción de índices bursátiles como herramienta decisoria de inversión. | es_CO |
| dc.type | http://purl.org/coar/resource_type/c_7a1f | es_CO |
| dc.date.accepted | 2017-12-15 | - |
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| dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | es_CO |
| dc.type.coarversion | http://purl.org/coar/resource_type/c_2df8fbb1 | es_CO |
| Aparece en las colecciones: | Ingeniería Industrial | |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| Pastrana_2017_TG.pdf | Pastrana_2017_TG | 624,19 kB | Adobe PDF | Visualizar/Abrir |
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